





import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    Tuple							)    ->     Tuple:
         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()



      @property
      def 						lowerCAmelCase__     (						self:    Dict							)    ->     Tuple:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =UNetaDModel(
             sample_size=(32, 64)		,	in_channels=1		,	out_channels=1		,	layers_per_block=2		,	block_out_channels=(128, 128)		,	down_block_types=("AttnDownBlock2D", "DownBlock2D")		,	up_block_types=("UpBlock2D", "AttnUpBlock2D")		,	)
         return model



      @property
      def 						lowerCAmelCase__     (						self:    int							)    ->     int:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =UNetaDConditionModel(
             sample_size=(64, 32)		,	in_channels=1		,	out_channels=1		,	layers_per_block=2		,	block_out_channels=(128, 128)		,	down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")		,	up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")		,	cross_attention_dim=10		,	)
         return model



      @property
      def 						lowerCAmelCase__     (						self:    Optional[int]							)    ->     List[Any]:
         '''simple docstring'''

         torch.manual_seed(0							)
         UpperCAmelCase_           =AutoencoderKL(
             sample_size=(128, 64)		,	in_channels=1		,	out_channels=1		,	latent_channels=1		,	layers_per_block=2		,	block_out_channels=(128, 128)		,	down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")		,	up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")		,	)
         UpperCAmelCase_           =UNetaDModel(
             sample_size=(64, 32)		,	in_channels=1		,	out_channels=1		,	layers_per_block=2		,	block_out_channels=(128, 128)		,	down_block_types=("AttnDownBlock2D", "DownBlock2D")		,	up_block_types=("UpBlock2D", "AttnUpBlock2D")		,	)
         return vqvae, unet






      @slow
      def 						lowerCAmelCase__     (						self:    Union[str, Any]							)    ->     List[str]:
         '''simple docstring'''

         UpperCAmelCase_           ="cpu"  # ensure determinism for the device-dependent torch.Generator
         UpperCAmelCase_           =Mel(
             x_res=self.dummy_unet.config.sample_size[1]		,	y_res=self.dummy_unet.config.sample_size[0]		,	)

         UpperCAmelCase_           =DDPMScheduler()
         UpperCAmelCase_           =AudioDiffusionPipeline(vqvae=_lowerCAmelCase		,	unet=self.dummy_unet		,	mel=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(42							)
         UpperCAmelCase_           =pipe(generator=_lowerCAmelCase		,	steps=4							)
         UpperCAmelCase_           =output.audios[0]
         UpperCAmelCase_           =output.images[0]

         UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(42							)
         UpperCAmelCase_           =pipe(generator=_lowerCAmelCase		,	steps=4		,	return_dict=_lowerCAmelCase							)
         UpperCAmelCase_           =output[0][0]

         assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
         assert (
             image.height == self.dummy_unet.config.sample_size[0]
             and image.width == self.dummy_unet.config.sample_size[1]
         )
         UpperCAmelCase_           =np.frombuffer(image.tobytes()		,	dtype="uint8"							)[:10]
         UpperCAmelCase_           =np.frombuffer(image_from_tuple.tobytes()		,	dtype="uint8"							)[:10]
         UpperCAmelCase_           =np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() == 0
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() == 0

         UpperCAmelCase_           =Mel(
             x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]		,	y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]		,	)

         UpperCAmelCase_           =DDIMScheduler()
         UpperCAmelCase_           =self.dummy_vqvae_and_unet
         UpperCAmelCase_           =AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]		,	unet=dummy_vqvae_and_unet[1]		,	mel=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         np.random.seed(0							)
         UpperCAmelCase_           =np.random.uniform(-1		,	1		,	((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)							)
         UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(42							)
         UpperCAmelCase_           =pipe(raw_audio=_lowerCAmelCase		,	generator=_lowerCAmelCase		,	start_step=5		,	steps=10							)
         UpperCAmelCase_           =output.images[0]

         assert (
             image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
             and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
         )
         UpperCAmelCase_           =np.frombuffer(image.tobytes()		,	dtype="uint8"							)[:10]
         UpperCAmelCase_           =np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() == 0

         UpperCAmelCase_           =self.dummy_unet_condition
         UpperCAmelCase_           =AudioDiffusionPipeline(
             vqvae=self.dummy_vqvae_and_unet[0]		,	unet=_lowerCAmelCase		,	mel=_lowerCAmelCase		,	scheduler=_lowerCAmelCase							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         np.random.seed(0							)
         UpperCAmelCase_           =torch.rand((1, 1, 10)							)
         UpperCAmelCase_           =pipe(generator=_lowerCAmelCase		,	encoding=_lowerCAmelCase							)
         UpperCAmelCase_           =output.images[0]
         UpperCAmelCase_           =np.frombuffer(image.tobytes()		,	dtype="uint8"							)[:10]
         UpperCAmelCase_           =np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() == 0

@slow
@require_torch_gpu
class   A							(						unittest.TestCase    ):



      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Dict:
         '''simple docstring'''

         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 						lowerCAmelCase__     (						self:    List[Any]							)    ->     Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_           =torch_device

         UpperCAmelCase_           =DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"							)
         UpperCAmelCase_           =pipe.to(_lowerCAmelCase							)
         pipe.set_progress_bar_config(disable=_lowerCAmelCase							)

         UpperCAmelCase_           =torch.Generator(device=_lowerCAmelCase							).manual_seed(42							)
         UpperCAmelCase_           =pipe(generator=_lowerCAmelCase							)
         UpperCAmelCase_           =output.audios[0]
         UpperCAmelCase_           =output.images[0]

         assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
         assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
         UpperCAmelCase_           =np.frombuffer(image.tobytes()		,	dtype="uint8"							)[:10]
         UpperCAmelCase_           =np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() == 0








'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						:  str				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  Optional[int]				  =						{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: List[str]       =    """lilt"""






							def __init__(       self       ,   __snake_case=3_0522       ,   __snake_case=768       ,   __snake_case=12       ,   __snake_case=12       ,   __snake_case=3072       ,   __snake_case="gelu"       ,   __snake_case=0.1       ,   __snake_case=0.1       ,   __snake_case=512       ,   __snake_case=2       ,   __snake_case=0.02       ,   __snake_case=1e-12       ,   __snake_case=0       ,   __snake_case="absolute"       ,   __snake_case=None       ,   __snake_case=4       ,   __snake_case=1024       ,   **__snake_case       ,   ):
											super().__init__(pad_token_id=__snake_case       ,   **__snake_case    )

											_SCREAMING_SNAKE_CASE					:			str							= vocab_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_size
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= num_hidden_layers
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= num_attention_heads
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= hidden_act
											_SCREAMING_SNAKE_CASE					:			Dict							= intermediate_size
											_SCREAMING_SNAKE_CASE					:			Tuple							= hidden_dropout_prob
											_SCREAMING_SNAKE_CASE					:			List[str]							= attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= max_position_embeddings
											_SCREAMING_SNAKE_CASE					:			Dict							= type_vocab_size
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= initializer_range
											_SCREAMING_SNAKE_CASE					:			Tuple							= layer_norm_eps
											_SCREAMING_SNAKE_CASE					:			str							= position_embedding_type
											_SCREAMING_SNAKE_CASE					:			List[str]							= classifier_dropout
											_SCREAMING_SNAKE_CASE					:			int							= channel_shrink_ratio
											_SCREAMING_SNAKE_CASE					:			str							= max_ad_position_embeddings




